The invention relates to the application of computers to the solution of target tracking problems, in particular sensing and following a target using radiant energy, where the tracking signals are processed to assess the probable accuracy of the tracking. More particularly, it solves the fundamental track monitoring problem of deciding whether a poor track is due to track loss or target maneuver.
Tracking targets in three-dimensional space using passive sensors, e.g., infrared sensors, requires at least two sensors separated along a base line to acquire range information. The target sensors supply elevation and azimuth information about detected targets but the information signals are contaminated by noise and clutter. Tracking multiple targets increases the complexity of the problem.
Poor tracking results can be caused by unrealized target maneuvering, by one sensor tracking a target different from the target being tracked by the other sensor, or by accumulated errors in one or both sensors.
An example of a tracking algorithm is the well known Kalman Filter Equation which estimates a state vector, X, by EQU X(k.vertline.k)=.phi.X(k-1.vertline.k-1)+K(k){H.phi.X(k-1.vertline.k-1)-z(k )}
where the quantity in braces is a residual vector, r, the difference between the predicted position and the measured position. The value of r is considered to be NID (normally and independently distributed). The equation predicts a next position, indicated by the index k, from a previous position, indicated by the index k-1, based on historical information about the target track.
Track monitoring can use a value derived from the Kalman Filter Equation, EQU d(k)=.alpha.d(k-1)+r.sup.T s.sup.-1 r
where
0&lt;.alpha..ltoreq.1, PA0 r is the residual vector, PA0 r.sup.T is the transpose of r, PA0 s is the covariance matrix of r (calculated in the Kalman Gain Equation), and PA0 s.sup.-1 is the inverse matrix of s.
The value of d(k) is distributed as a chi-square random variable with n(1+.alpha.)/(1-.alpha.) degrees of freedom where n is the number of measurement components. When d(k) exceeds a threshold value, e.g., a 95% confidence interval, the tracking may be considered to be poor.
Whether poor tracking is caused by target maneuvering or track loss cannot be accurately or readily determined from the value of d(k) alone. The invention supplies an additional statistic or parameter to evaluate tracking success more accurately and definitively.
Prior art techniques for dealing with the errors that can occur are slow or tend to explode in size (such as tracking all possible positions of an uncertain target), both of which overload the processing system executing the tracking algorithm. Some prior art methods use only d(k). Some use Bayesian probabilities. Others use procedures requiring extensive computer time.
U.S. Pat. No. 4,914,604 discloses a neural network analog associative processor that analyzes a plurality of angles from a plurality of sensors (at least three) forming multiple possible intersections representing targets. The processor comprises a multilayer substrate forming an analog of the actual sensors' physical positions. False targets (ghosts) are identified and eliminated by counting pulses, one from each intersect, from the other intersects until the number of remaining intersects equals the total number of targets.
U.S. Pat. No. 4,529,316 discloses a method of identifying a false data point representing an erroneous depth location. A projector directs light onto a point on a surface. The light is reflected from the surface to two sensors and the 3-dimensional position of the point is computed by conventional techniques from the known locations of the sensors and projector. The calculated position from each sensor is compared. If they differ significantly, the computed position is determined to be false.
U.S. Pat. No. 4,326,259 shows a perceptron-like device for identifying targets from multiple input sources by dividing space into two or more regions by constructing lines, planes, or hyperplanes. The space is divided by adapting the device to recognize different classes of events.
U.S. Pat. No. 4,333,077 discloses a means to determine whether a radar surveillance unit and an optical tracking unit are pointed in the same direction using a phase comparator responsive to the position indicator of each unit.
U.S. Pat. No. 2,404,243 discloses the use of directional microphones to coordinate a plurality of tracking or other devices on a common point such as search lights or guns on a target aircraft. It shows an electromechanical apparatus based on a scale model for making the calculations.
U.S. Pat. No. 3,445,847 shows an apparatus for performing trigonometric functions to make geometrical determinations of one or more points.
U.S. Pat. No. 3,754,249 shows the combination of a television camera and laser beam to lock onto a target for guiding a missile to the target.
U.S. Pat. No. 4,622,458 teaches the use of several independent track control stations coupled to a central station and operating in real time to track moving targets and evaluate trajectories.
Reference may be made to "Trajectory Plotting by Triangulation in Passive Sonar" (INSPEC Abstract Number B88022225) by J. Durif and L. Kopp for a discussion of optimal tracks association testing to match tracks where two sensors are tracking multiple targets using triangulation.
None of the prior art patents or literature shows or suggests the use of the criteria of the invention. The criteria used in the prior art do not provide as good a decision parameter and require more computer time for computation.
According to the invention, a method of evaluating the tracking of a maneuvering target using at least two sensors stores the target data received from each sensor and predicts a target position using the data. Next, a track quality statistic is derived from the stored target data and an inclination angle statistic is determined from the same data. Whether a target track is acceptable is evaluated based on the track quality statistic and the inclination angle statistic.